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NIKE Data Scientist Interview Questions and Answers

Updated 27 Sep 2024

NIKE Data Scientist Interview Experiences

1 interview found

Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-

I applied via Job Portal

Round 1 - Coding Test 

A/B Testing, data structures

Interview Preparation Tips

Interview preparation tips for other job seekers - Study a/b testing

Interview questions from similar companies

Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. What is supervised learning
  • Ans. 

    Supervised learning is a type of machine learning where the model is trained on labeled data to make predictions or decisions.

    • Uses labeled training data to learn the mapping between input and output variables

    • The model is trained on a dataset where the correct output is known

    • Examples include classification and regression tasks

  • Answered by AI
  • Q2. What is overfitting
  • Ans. 

    Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern.

    • Overfitting happens when a model is too complex and captures noise in the training data.

    • It leads to poor generalization on new, unseen data.

    • Techniques to prevent overfitting include cross-validation, regularization, and early stopping.

    • Example: A decision tree with too many branches that perfectly fits the training d

  • Answered by AI

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
Selected Selected

I applied via LinkedIn and was interviewed before Jan 2024. There were 4 interview rounds.

Round 1 - Case Study 

Case Study was related to customer propensity to buy.

Round 2 - Technical 

(2 Questions)

  • Q1. Linear Regression Assumptions
  • Ans. 

    Linear regression assumptions include linearity, independence, homoscedasticity, and normality.

    • Assumption of linearity: The relationship between the independent and dependent variables is linear.

    • Assumption of independence: The residuals are independent of each other.

    • Assumption of homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.

    • Assumption of normality: The resid...

  • Answered by AI
  • Q2. ML Algorithms and Evaluation Metrics.
Round 3 - One-on-one 

(1 Question)

  • Q1. What is VIF(variance inflation factor)
  • Ans. 

    VIF is a measure of multicollinearity in regression analysis, indicating how much the variance of an estimated regression coefficient is increased due to collinearity.

    • VIF values greater than 10 indicate high multicollinearity

    • VIF is calculated for each predictor variable in a regression model

    • VIF is calculated as 1 / (1 - R^2) where R^2 is the coefficient of determination from regressing a predictor on all other predicto

  • Answered by AI
Round 4 - HR 

(1 Question)

  • Q1. Why do you want to join?
  • Ans. 

    I am impressed by your company's innovative projects and collaborative work culture.

    • I admire the company's commitment to cutting-edge technology and data-driven solutions.

    • I am excited about the opportunity to work with a talented team of data scientists and researchers.

    • Your company's reputation for fostering a collaborative and inclusive work environment is appealing to me.

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Intermediate knowledge of ML algos and evaluation metrics is must. Python and SQL hand-on is required.
Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. How to reduce model inference latency
  • Ans. 

    To reduce model inference latency, optimize model architecture, use efficient algorithms, batch processing, and deploy on high-performance hardware.

    • Optimize model architecture by reducing complexity and removing unnecessary layers

    • Use efficient algorithms like XGBoost or LightGBM for faster predictions

    • Implement batch processing to make predictions in bulk rather than one at a time

    • Deploy the model on high-performance har

  • Answered by AI
  • Q2. Different sql joins and their difference
  • Ans. 

    SQL joins are used to combine rows from two or more tables based on a related column between them.

    • INNER JOIN: Returns rows when there is at least one match in both tables.

    • LEFT JOIN: Returns all rows from the left table and the matched rows from the right table.

    • RIGHT JOIN: Returns all rows from the right table and the matched rows from the left table.

    • FULL JOIN: Returns rows when there is a match in one of the tables.

    • SEL

  • Answered by AI

Skills evaluated in this interview

I was interviewed in Apr 2021.

Round 1 - Video Call 

(2 Questions)

Round duration - 60 Minutes
Round difficulty - Medium

I was asked two questions in this round . More emphasis was given on the theoretical aspect of the subject in this round .

  • Q1. How can you tune the hyperparameters of the XGBoost algorithm?
  • Ans. 

    Hyperparameters of XGBoost can be tuned using techniques like grid search, random search, and Bayesian optimization.

    • Use grid search to exhaustively search through a specified parameter grid

    • Utilize random search to randomly sample hyperparameters from a specified distribution

    • Apply Bayesian optimization to sequentially choose hyperparameters based on the outcomes of previous iterations

  • Answered by AI
  • Q2. Can you explain the hyperparameters in the XGBoost algorithm?
  • Ans. 

    Hyperparameters in XGBoost algorithm control the behavior of the model during training.

    • Hyperparameters include parameters like learning rate, max depth, number of trees, etc.

    • They are set before the training process and can greatly impact the model's performance.

    • Example: 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100

  • Answered by AI
Round 2 - Video Call 

(1 Question)

Round duration - 50 Minutes
Round difficulty - Medium

This round basically tested some fundamental concepts related to Machine Learning and proper ways to implement a model.

  • Q1. What is the difference between Ridge and LASSO regression?
  • Ans. 

    Ridge and LASSO regression are both regularization techniques used in linear regression to prevent overfitting by adding penalty terms to the cost function.

    • Ridge regression adds a penalty term equivalent to the square of the magnitude of coefficients (L2 regularization).

    • LASSO regression adds a penalty term equivalent to the absolute value of the magnitude of coefficients (L1 regularization).

    • Ridge regression tends to sh...

  • Answered by AI
Round 3 - Video Call 

(1 Question)

Round duration - 50 Minutes
Round difficulty - Medium

This round was based on some basic concepts revolving around Deep Learning .

  • Q1. What are outlier values and how do you treat them?
  • Ans. 

    Outlier values are data points that significantly differ from the rest of the data, potentially affecting the analysis.

    • Outliers can be identified using statistical methods like Z-score or IQR.

    • Treatment options include removing outliers, transforming the data, or using robust statistical methods.

    • Example: In a dataset of salaries, a value much higher or lower than the rest may be considered an outlier.

  • Answered by AI
Round 4 - HR 

(2 Questions)

Round duration - 30 Minutes
Round difficulty - Easy

This is a cultural fitment testing round .HR was very frank and asked standard questions. Then we discussed about my role.

  • Q1. What do you know about the company?
  • Q2. Why should we hire you?

Interview Preparation Tips

Eligibility criteriaAbove 3 years of experienceWalmart interview preparation:Topics to prepare for the interview - Statistics , SQL , Machine Learning Algorithms , Data Wrangling , Neural NetworksTime required to prepare for the interview - 3 monthsInterview preparation tips for other job seekers

Tip 1 : Must do Previously asked Interview as well as Online Test Questions.
Tip 2 : Do at-least 2 good projects and you must know every bit of them.

Application resume tips for other job seekers

Tip 1 : Have at-least 2 good projects explained in short with all important points covered.
Tip 2 : Every skill must be mentioned.
Tip 3 : Focus on skills, projects and experiences more.

Final outcome of the interviewSelected

Skills evaluated in this interview

I was interviewed in Apr 2021.

Interview Questionnaire 

9 Questions

  • Q1. How can you tune the hyper parameters of XGboost,Random Forest,SVM algorithm?
  • Ans. 

    Hyperparameters of XGBoost, Random Forest, and SVM can be tuned using techniques like grid search, random search, and Bayesian optimization.

    • For XGBoost, important hyperparameters to tune include learning rate, maximum depth, and number of estimators.

    • For Random Forest, important hyperparameters to tune include number of trees, maximum depth, and minimum samples split.

    • For SVM, important hyperparameters to tune include ke...

  • Answered by AI
  • Q2. What do these hyper parameters in the above mentioned algorithms actually mean?
  • Ans. 

    Hyperparameters are settings that control the behavior of machine learning algorithms.

    • Hyperparameters are set before training the model.

    • They control the learning process and affect the model's performance.

    • Examples include learning rate, regularization strength, and number of hidden layers.

    • Optimizing hyperparameters is important for achieving better model accuracy.

  • Answered by AI
  • Q3. Difference between Ridge and LASSO and their geometric interpretation.
  • Ans. 

    Ridge and LASSO are regularization techniques used in linear regression to prevent overfitting.

    • Ridge adds a penalty term to the sum of squared errors, which shrinks the coefficients towards zero but doesn't set them exactly to zero.

    • LASSO adds a penalty term to the absolute value of the coefficients, which can set some of them exactly to zero.

    • The geometric interpretation of Ridge is that it adds a constraint to the size...

  • Answered by AI
  • Q4. How to fit a time series model? State all the steps you would follow.
  • Ans. 

    Steps to fit a time series model

    • Identify the time series pattern

    • Choose a suitable model

    • Split data into training and testing sets

    • Fit the model to the training data

    • Evaluate model performance on testing data

    • Refine the model if necessary

    • Forecast future values using the model

  • Answered by AI
  • Q5. RNN,CNN and difference between these two.
  • Ans. 

    RNN and CNN are neural network architectures used for different types of data.

    • RNN is used for sequential data like time series, text, speech, etc.

    • CNN is used for grid-like data like images, videos, etc.

    • RNN has feedback connections while CNN has convolutional layers.

    • RNN can handle variable length input while CNN requires fixed size input.

    • Both can be used for classification, regression, and generation tasks.

  • Answered by AI
  • Q6. Two Case studies related to optimisation. One was cost optimization and other one was Revenue optimization. What data would you look at to solve all these. How would you form the objective function.
  • Ans. 

    Answering a question on data and objective function for cost and revenue optimization case studies.

    • For cost optimization, look at data related to expenses, production costs, and resource allocation.

    • For revenue optimization, look at data related to sales, customer behavior, and market trends.

    • Objective function for cost optimization could be minimizing expenses while maintaining quality.

    • Objective function for revenue opt...

  • Answered by AI
  • Q7. Live coding on Time Series Modelling
  • Q8. There were some HR questions as well like how would you make someone understand the difference between a classification problem and a prediction problem.
  • Q9. Where do you see yourself in 3 years?

Interview Preparation Tips

Interview preparation tips for other job seekers - I was asked questions from almost every field in Data Science. One has to be very technically sound and has to have clear understanding of all the ML algorithms.

If you don't know something,better to mention it clearly.

All the very best!

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Referral and was interviewed before Sep 2022. There were 6 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all Resume tips
Round 2 - Technical 

(1 Question)

  • Q1. All the questiones were asked around CV. Mostly problems related to ML, DL, NLP, mathematics behind the algorithms, case studies, alternate solutions of popular use cases etc.
Round 3 - Technical 

(1 Question)

  • Q1. Same as round 1 but this round involved a lot of mathematical functions and derivations of several aspects of ML and DL. Also a lot of case studies were involved
Round 4 - Coding Test 

Had to share my screen and they gave live problems to test my knowledge in python

Round 5 - One-on-one 

(1 Question)

  • Q1. Call with hiring manager mostly on my CV and a lot of case studies.
Round 6 - HR 

(1 Question)

  • Q1. Typical HR round questions
Interview experience
3
Average
Difficulty level
Moderate
Process Duration
More than 8 weeks
Result
Not Selected

I applied via LinkedIn and was interviewed in Oct 2023. There were 3 interview rounds.

Round 1 - HR 

(1 Question)

  • Q1. Why do you want to leave your current job?
Round 2 - Coding Test 

SQL coding question. Medium level

Round 3 - Case Study 

Explain my project and then case study regarding launching new apps

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
Selected Selected

I applied via LinkedIn and was interviewed before May 2023. There were 4 interview rounds.

Round 1 - Technical 

(3 Questions)

  • Q1. Linear regression
  • Q2. Probability related questions
  • Q3. Sampling and AB testing
Round 2 - Technical 

(2 Questions)

  • Q1. Backpropagation in neural network
  • Ans. 

    Backpropagation is a method used to train neural networks by adjusting the weights based on the error in the output.

    • Backpropagation involves calculating the gradient of the loss function with respect to the weights of the network.

    • The gradient is then used to update the weights in the opposite direction to minimize the error.

    • This process is repeated iteratively until the network converges to a solution.

    • Backpropagation i...

  • Answered by AI
  • Q2. Clustering (k-means, DB scan)
Round 3 - Coding Test 

1 question on array (sorting related), 1 question on string (hard problem)

Round 4 - Behavioral 

(1 Question)

  • Q1. Behavioral questions
Interview experience
2
Poor
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Coding Test 

3 Leet code mediums in 30 mins.

Round 2 - Technical 

(3 Questions)

  • Q1. 5 ML questions in 10 mins
  • Q2. 5 Stats question in 10 mins
  • Q3. 3 LC mediums in 30 minutes
  • Ans. 

    LC mediums refer to LeetCode mediums, which are medium difficulty coding problems on the LeetCode platform.

    • LC mediums are coding problems with medium difficulty level on LeetCode platform.

    • Solving 3 LC mediums in 30 minutes requires good problem-solving skills and efficient coding techniques.

    • Examples of LC mediums include 'Longest Substring Without Repeating Characters' and 'Container With Most Water'.

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Pray
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NIKE Interview FAQs

How many rounds are there in NIKE Data Scientist interview?
NIKE interview process usually has 1 rounds. The most common rounds in the NIKE interview process are Coding Test.

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NIKE Data Scientist Interview Process

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